Abstract: A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.

Abstract: A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.

Abstract: A method for configuring a manufacturing plant includes generating a set of initial production schedules for manufacturing an entity, determining a set of consistent production schedules given each of the initial production schedules, selecting an instance of the consistent production schedules, generating a schedule for manufacturing instances of the entity, and operating the manufacturing plant using the schedule.

Abstract: A method for configuring a manufacturing plant includes generating a set of initial production schedules for manufacturing an entity, determining a set of consistent production schedules given each of the initial production schedules, selecting an instance of the consistent production schedules, generating a schedule for manufacturing instances of the entity, and operating the manufacturing plant using the schedule

Abstract: A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.

Abstract: A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.

Abstract: Automated feature construction for algorithm portfolios in machine learning is provided. A gray scale image is generated from a text representing a problem instance. The gray scale image is rescaled or reshaped to a predefined size that is smaller than an initial size of the gray scale image. The rescaled gray scale image represents features of the problem instance. The rescaled gray scale image is input as features to a machine learning-based convolutional neural network. Based on the rescaled gray scale image, the machine learning-based convolutional neural network is automatically trained to learn to automatically determine one or more problem solvers from a portfolio of problem solvers suited for solving the problem instance.